Dynamic Object-aware SLAM - In order to track dynamic objects' pose and decouple them from static background, frame-model Iterative Closest Point (ICP) combined with color [1]}, [2]}, [3]}, [4]}, probabilistic data association [5]}, or 3D level-set likelihood maximization [6]} has been applied. Object models are simultaneously reconstructed on-the-fly by aggregating the observed RGB-D data with the newly tracked pose. Nevertheless, frame-model tracking can be challenging for object reconstruction, since errors in pose estimation transfer to the reconstructed model and adversely affect the subsequent tracking [7]}. This work does not fuse observed frames but instead maintains them as nodes in a pose graph, allowing to correct previously erroneous estimates, and reduces drift in long-term tracking. The aforementioned SLAM-family approaches may also face challenges in robot manipulation setups that involve small, textureless, flat or shiny objects due to the dearth of sufficient correspondences between the pair of consecutive frames. To ameliorate this issue, BundleTrack searches correspondences among current and multiple historical frames, consisting of both feature and geometric terms, as the edges in the pose graph. Its effectiveness has been shown in extensive experiments including for such challenging manipulation scenarios.
